Weather, Climatology and Solid Earth sciences (WCES)
Sciences related to Earth encompass a wide range of disciplines from the study of the atmosphere, the oceans, the biosphere to issues related to the solid part of the planet.
A key trend in this domain is the move from current climate models towards full Earth System Models.
Today it is clear that models must also include more sophisticated representations of non-physical processes and subsystems, which are of major importance for long-term climate development, like the carbon cycle. Scientists are strongly interested to know the sensitivity of predictions not only to unresolved physical processes (as, e.g., the cloud feedbacks mentioned above), but also to non- physical ones, like those related to biology and chemistry (including, for example, those involving the land surfaces, and -greenhouse gases reactions).
It should be noted that including the representation of biogeochemical cycles using different biochemical tracers and aerosols typically increase computing time by a factor of 5 to 20 (depending on the complexity of the parameterizations and the number of tracers). An increase of computing power by a factor of 5 to 20 is then required to better account for the complexity of the system.
Earth sciences are multiple, from weather prediction to air quality and to climate change forecast, from ocean prediction to natural hazards, such as seismic and volcanic hazards. Among all these issues WCES particularly requires exascale capacity in two domains:
- weather and climate, which share some similarities
- solid earth
Challenges: speaking of climate change, beyond greenhouse warming analysis, it is becoming crucial to shift from predictions to forecasts, ie changing scale of time from yearly to ten-yearly and learning evolution from initial ocean state to future.
Studying thermal convection, from the lower to the upper layers of the atmosphere, requires «subgrid scale parametrizations». Uncertainty of such modelisation will directly impact the quality of results of climate simulations.
Climate simulation will require an estimated grid size slightly smaller or equal to ca. 1 kilometer. This is a major challenge in terms of volume of data to be processed.
By-en-large, challenges impacting the two domains can be summarized as follows:
- Scientific data management: visualization, parallel files systems, parallelization strategies and frameworks for data analysis, programming models for big data, data compression, data mining, data quality, data provenance, metadata management, etc.
- Earth Sciences Modelling: finer resolution, development, evaluation of parameterization well adapted to those fine resolution, fast run for lower-resolution models, uncertainty quantification, quality control, new approaches (e.g., new grids and new dynamical cores), etc.
- Cross-cutting issues: multi-physics coupling software for 105-106 cores, resilience, and performance of individual modules and of complete coupled systems, parallel I/O
Path: improving scalability of applications, developing dynamical cores, coupling multiple codes, assessing uncertainties of the models.
Roadmap: Climate modelling requires very high spatial resolution for reaching satisfactory simulation of cloud and convection processes. As a step forward to reaching the longer-term, 1-km resolution,objective, the image shown in this section is a simulation at the spatial resolution of 25 km. It has been performed during the UPSCALE project using the Met Office Global Atmosphere Unified Model on the PrACE Tier0 machine Hermit